Artificial intelligence systems include expert systems and case-based systems. An expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented as a segmented base of if-then rules, where there is no other explicit connection among the rules.
Case-based systems model events and their causalities using situation-action codes. These systems solve new problems based on the solutions of similar past problems.
In both expert systems and case-based systems, similar cases cannot be predicted but can only be acquired experientially. As a result, in both conventional expert and case-based systems, all acquired knowledge is mutually random. Neither expert systems nor case based systems express knowledge using natural language. This means that the representation of knowledge by these systems is neither easy to understand or extend.
While methodologies have been proposed for computational natural language understanding based on hidden-layer neural networks, they cannot tractably learn and cannot retain all of the fundamental memories. Also, a deep learning neural network cannot explain its decisions or actions.
Rule-based approaches to natural language processing need to deal with the fact that there are more exceptions than rules. Rule-based approaches have not been successful in learning these exceptions to date. Nevertheless, attempts have been made to overcome this limitation through the use of brute force (i.e., hand-coding virtually everything which a person might ask in a specific domain). Such approaches do not benefit from domain transference. Thus, they could never pass the Turing Test if one moves slightly askew of the domain of discourse.
In view of the above, it would be desirable to address shortcomings of conventional expert systems and case-based systems.